Seizing opportunities while managing challenges

The commercial insurance industry typically handles structured data (claims data, premium records and actuarial calculations), unstructured data (emails, documents, spreadsheets, videos of property damage and client-provided images) and judgements (e.g. in deriving assumptions for pricing, reserving and capital calculations or in underwriters using judgement based on other sources to amend a technical actuarial price). GenAI has demonstrated its adaptability across all these formats, particularly when dealing with unstructured data and learning how to replicate judgements, making it a valuable tool to improve operational efficiencies and customer engagement, and enhance risk management capabilities. 

Nevertheless, while the advantages of GenAI are significant, there are notable challenges (e.g. model risk, data quality, potential for hallucinations, data privacy, bias, explainability, regulatory compliance and others). As GenAI technology advances, it’s likely to become an essential component of modern insurance operations, leading to enhanced personalisation, quicker service and improved outcomes for insurers and policyholders alike.

This article delves into the transformative potential of GenAI across the commercial insurance value chain. It also seeks to address the associated risks and challenges, including ethical considerations, as well as some potential strategies that commercial insurers can take to mitigate these risks and challenges. Although this paper is oriented towards Commercial Insurers, a number of themes / topics outlined remain true to Retail insurers as well.

GenAI can transform the commercial insurance value chain by automating tasks like drafting policy documents, analysing claims and creating more accurate risk models. By synthesising large volumes of data, it helps insurers refine coverage, speed up processing and enhance customer engagement. Overall, GenAI can streamline operations, optimise underwriting and claims workflows, and support data-driven decision-making in the commercial insurance sector. 

While the advantages of GenAI are significant, there are notable challenges (e.g model risk, data quality, potential for hallucinations, data privacy, bias, explainability, regulatory compliance, and others). To mitigate these risks, it's imperative to implement robust data governance, ensuring transparency in AI decision making, using diverse and representative training data, and developing ethical AI policies. Insurers must navigate evolving AI regulations, ensure accuracy and domain expertise in models, and balance automation with human oversight. Organisations should invest in AI literacy, continuously monitor AI outputs and adopt human-in-the-loop systems to maintain oversight. Balancing innovation with responsible oversight is key to leveraging GenAI effectively while safeguarding trust, compliance and ethical standards in commercial insurance.

GenAI is poised to provide commercial insurers with the opportunity to introduce innovative risk management solutions to their customers, potentially creating new revenue streams, mitigating future claims risks and/or creating differentiating capabilities which will set them apart from their competition. By identifying vulnerabilities early on, insurers can reduce the frequency and severity of future claims while creating new revenue sources beyond their traditional risk transfer offerings. Some of these services can include AI-powered tools to audit and validate GenAI outputs for originality and compliance with intellectual property (IP) regulations, audits to ensure clients’ GenAI implementations meet industry standards and legal requirements, training programmes to help clients identify and mitigate biases in GenAI outputs, and support with cyber risk from malicious use of AI models, amongst others.

By expanding risk management services in tandem with GenAI-centric insurance coverage, insurers can differentiate themselves as trusted partners to clients in GenAI. These offerings help corporate clients deploy GenAI confidently while mitigating emerging risks in a fast-changing regulatory and technological landscape.

GenAI is poised to reshape the commercial insurance landscape, highlighting its ability to drive significant operational efficiencies, enhance risk assessment and improve customer engagement through automation and the intelligent generation of content. As insurers increasingly face competitive pressures and rising consumer expectations, leveraging GenAI can optimise key processes such as underwriting, pricing, claims handling, reserving, catastrophe modelling and model development more generally, ultimately leading to a more agile and responsive insurance landscape. However, as with any transformative technology, GenAI brings notable challenges that can span ethical and regulatory complexities as well as potential security risks given the possibilities of deepfake fraud or data leakage. To mitigate these risks, insurers must develop rigorous governance frameworks, create testing and validation of their GenAI outputs, invest in robust data architectures and maintain a balance between automation and human-led expert oversight. 

The near future holds significant promise for insurers that strategically incorporate GenAI. Ultimately, commercial insurers who proactively adopt GenAI while carefully managing its risks will be best positioned to thrive amid a rapidly evolving insurance landscape.